# Get familiar with medical imaging datasets

A guide on dealing with medical images

At AIOZ, we have been involved in several projects applying computer science to assist the medical field. Those kinds of projects are very demanding recently, due to the impact of deep learning. Besides the robotics project to help doctors transport medical equipment in isolation areas safely, we also participate in academic research which utilizes machine learning in general and deep learning in particular. With some articles published in this field, we believe we can share some helpful tutorials to assist the community. Thus, in this post, we will introduce a sample of a medical image and how to load, visualize and preprocess it.

## #Understand the image

Recently, we have worked on 2 types of medical imaging, namely Liver CT scan and Brain MRI, for the deformable registration problem. However, in this tutorial, we only introduce a sample of the Liver CT, which belongs to the MSD dataset. MRI data might be introduced in another tutorial.

The MSD dataset is a labeled dataset for the medical image segmentation task (tumor segmentation for liver CT scan). Let's explore this CT image data. We prepare a sample that includes an image and its label (the segmentation of the tumors).

!ls liverCT
liver_0_label.nii.gz  liver_0.nii.gz

This is NIfTI type of data, commonly used to store medical images. To load this data, we use the nibabel library for python.

!pip install nibabel

Let import some necessary libraries.

import nibabel as nibimport osimport numpy as np

After loading the image, we use the get_data() method to get the numeric data.

root_dir = 'liverCT'img = nib.load(os.path.join(root_dir, 'liver_0.nii.gz'))label = nib.load(os.path.join(root_dir, 'liver_0_label.nii.gz'))img_data = img.get_data()label_data = label.get_data()

Let view some information of the data

img_data.shape
(512, 512, 75)
label_data.shape
(512, 512, 75)
np.mean(img_data), np.max(img_data), np.min(img_data)
(-523.4203, 1410.0, -1024.0)
np.mean(label_data), np.max(label_data), np.min(label_data)
(0.028239847819010417, 2, 0)

As can be seen, there are two kinds of tumor in this liver. Let see where a tumor is:

np.where(label_data == 1)
(array([133, 133, 133, ..., 441, 441, 441]), array([334, 335, 335, ..., 231, 232, 233]), array([65, 64, 65, ..., 63, 63, 63]))

Now, let show a slice of the liver that contains tumors.

import skimage.io as iodef show_volume_slice(data):    io.imshow(data, cmap = 'gray')    io.show()
show_volume_slice(img_data[133])

show_volume_slice(label_data[133])
/home/quangminh/.virtualenvs/rlenv/lib/python3.6/site-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.  lo, hi, cmap = _get_display_range(image)

## #A handy tool for processing medical images

Medical images are special and how to preprocess them is also complex. Moreover, applying training techniques of deep learning requires the processing to be synchronous, fast, and convenient. One of the tools to support medical imaging is monai, an open-source framework developing healthcare imaging training workflows in a native PyTorch paradigm.

Indeed, monai supports DataSet and DataLoader objects very similar to Pytorch. The feature of the DataLoader is to allow transformations to be performed while sampling the data. With special transformations for medical images, monai allows the entire process of loading and processing data to go smoothly without too many implementations. However, the input data needs to be defined as a list of dictionaries as follows:

data_dicts = [    {'image': 'liverCT/liver_0.nii.gz',     'label': 'liverCT/liver_0_label.nii.gz'}]
!pip install monai

Let import Dataset, DataLoader and some essential transformations. In fact, with the CropForegroundd transformation, we are able to get rid of uninformative data.

from monai.data import Dataset, DataLoaderfrom monai.transforms import CropForegroundd, LoadImaged, AddChanneld, Compose
# define the transformstransforms = Compose([                LoadImaged(keys=["image", "label"]),                AddChanneld(keys=["image", "label"]),                CropForegroundd(keys=["image", "label"], source_key="image")            ])
liver_dataset = Dataset(data=data_dicts,                        transform=transforms)
liver_loader = DataLoader(liver_dataset, num_workers=0, batch_size=1)

Let get a sample and visualize it.

sample = next(iter(liver_loader))
sample.keys()
dict_keys(['image', 'label', 'image_meta_dict', 'label_meta_dict', 'foreground_start_coord', 'foreground_end_coord', 'image_transforms', 'label_transforms'])
sample['image'].shape
torch.Size([1, 1, 500, 355, 75])
show_volume_slice(sample['image'][0][0].numpy()[133])

As we can see, the image is cropped according to the foreground.

## #Conclusion

To sum up, in this tutorial, we introduce a sample of LiverCT scan images and a brief guiding to preprocess them. Although this tutorial is not sufficient enough for the whole medical image pipeline of deep learning, we hope that it can be a good start for you to explore more in the field.